长江中下游6—7月降水的降尺度模型

基于偏相关的强迫因子选取方法,以长江中下游6—7月降水为例,进行了降水变率的归因分析,并建立了相应的统计降尺度模型。结果表明,影响长江中下游6—7月降水的强迫因子主要有两个:西太平洋850 h Pa的位势高度(W_(PH8))和黑潮延伸区的海表温度(K_(SST))。W_(PH8)反映的是西太平洋副热带高压对长江中下游降水的影响;K_(SST)反映了黑潮延伸区的变率。基于这两个因子的线性降尺度模型能较好地拟合长江中下游6—7月的降水,在独立检验和模式检验阶段,模型体现出了可靠性,因而可用于长江中下游降水的季节预测。     

Performance assessment of three convective parameterization schemes in WRF for downscaling summer rainfall over South Africa

Austral summer rainfall over the period 1991/1992 to 2010/2011 was dynamically downscaled by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model biases for the rainfall were evaluated over the South Africa as a whole and its nine provinces separately by employing three different convective parameterization schemes, namely the (1) Kain–Fritsch (KF), (2) Betts–Miller–Janjic (BMJ) and (3) Grell–Devenyi ensemble (GDE) schemes. All three schemes have generated positive rainfall biases over South Africa, with the KF scheme producing the largest biases and mean absolute errors. Only the BMJ scheme could reproduce the intensity of rainfall anomalies, and also exhibited the highest correlation with observed interannual summer rainfall variability. In the KF scheme, a significantly high amount of moisture was transported from the tropics into South Africa. The vertical thermodynamic profiles show that the KF scheme has caused low level moisture convergence, due to the highly unstable atmosphere, and hence contributed to the widespread positive biases of rainfall. The negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity simulated by the GDE scheme resulted in negative rainfall biases, especially over the Limpopo Province. In terms of rain rate, the KF scheme generated the lowest number of low rain rates and the maximum number of moderate to high rain rates associated with more convective unstable environment. KF and GDE schemes overestimated the convective rain and underestimated the stratiform rain. However, the simulated convective and stratiform rain with BMJ scheme is in more agreement with the observations. This study also documents the performance of regional model in downscaling the large scale climate mode such as El Niño Southern Oscillation (ENSO) and subtropical dipole modes. The correlations between the simulated area averaged rainfalls over South Africa and Nino3.4 index were ?0.66, ?0.69 and ?0.49 with KF, BMJ and GDE scheme respectively as compared to the observed correlation of ?0.57. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Niña (El Niño) and the BMJ scheme is closest to the observed variability. Also, the model showed good skill in simulating the excess rainfall over South Africa that is associated with positive subtropical Indian Ocean Dipole for the DJF season 2005/2006.     

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation(APEC) Climate Center(APCC) multi-model ensemble(MME) in predicting station-scale rainfall in South China(SC) in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME's ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output(DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.     

Moisture transport over the Arabian Sea associated with summer rainfall over Pakistan in 1994 and 2002

In this study,we aimed to elucidate the critical role of moisture transport affecting monsoon activity in two contrasting summers over the Arabian Sea during the years 1994,a relatively wet year,and 2002,a relatively dry year.A comprehensive diagnostic evaluation and comparisons of the moisture fields were conducted;we focused on the precipitation and evaporation as well as the moisture transport and its divergence or convergence in the atmosphere.Monthly mean reanalysis data were obtained from the National Centers for Environmental Prediction(NCEP-I and-II).A detailed evaluation of the moisture budgets over Pakistan during these two years was made by calculating the latent energy flux at the surface(E P) from the divergence of the total moisture transport.Our results confirm the moisture supply over the Arabian Sea to be the major source of rainfall in Pakistan and neighboring regions.In 1994,Pakistan received more rainfall compared to 2002 during the summer monsoon.Moisture flow deepens and strengthens over Arabian Sea during the peak summer monsoon months of July and August.Our analysis shows that vertically integrated moisture transport flux have a significant role in supplying moisture to the convective centers over Pakistan and neighboring regions from the divergent regions of the Arabian Sea and the Bay of Bengal.Moreover,in 1994,a deeper vertically integrated moisture convergence progression occurred over Pakistan compared to that in 2002.Perhaps that deeper convergence resulted in a more intense moisture depression over Pakistan and also caused more rainfall in 1994 during the summer monsoon.Finally,from the water budget analysis,it has been surmised that the water budget was larger in 1994 than in 2002 during the summer monsoon.     

Experimental 15-day-lead statistical forecast of intraseasonal summer monsoon rainfall over Eastern China

1996年长江中下游、2002年华中南部以及2006年华南沿海地区出现了显著洪涝,并均伴随强季节内降水活动。在30–60天和10–20天这两个时间尺度上,季节内降水异常均与亚洲季风区大气季节内振荡密切相关。本文选取亚洲季风区大气季节内振荡的关键要素作为预报因子,应用贝叶斯小波频段方案对季节内降水进行了预报时效为15天的延伸期预测。该方案对这三年的季节内降水中心的预测水平均较好,预测与观测的季节内降水相关系数均在0.6以上。     

WRF模式对中国夏季降水的动力降尺度模拟研究

采用NCEP的FNL再分析资料驱动WRF模式,对中国10 a(2000—2009年)夏季降水进行双重动力降尺度(双重嵌套)模拟,将子、母区域模拟结果和观测进行对比,以检验双重动力降尺度对中国夏季降水模拟的"增值"能力。结果表明:单重动力降尺度(单重嵌套)方法能较好模拟出中国10 a夏季平均降水的空间分布,对季风雨带"北跳"特征模拟较好,但模拟降水具有系统性正偏差。在母区域的强迫下,双重动力降尺度模拟的降水分布与单重动力降尺度相比,没有发生根本性变化。但由于子区域的分辨率要高于母区域,双重动力降尺度比单重动力降尺度能提供更多有价值的降水细节。双重动力降尺度的这种"增值"能力存在地域依赖性,在华南地区和江淮地区,双重动力降尺度模拟出的降水分布、量值和逐日演变都要好于单重动力降尺度。但在华北地区,双重动力降尺度没有表现出明显的"增值"。     

A timescale decomposed threshold regression downscaling approach to forecasting South China early summer rainfall

A timescale decomposed threshold regression(TSDTR) downscaling approach to forecasting South China early summer rainfall(SCESR) is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR.The two models are developed based on the partial least squares(PLS) regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915–84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation(PDO). On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985–2006, compared to other simpler approaches. This study suggests that the TSDTR approach,considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.     

A comparison of three multi-site statistical downscaling models for daily rainfall in the North China Plain

Three statistical downscaling methods (conditional resampling statistical downscaling model: CR-SDSM, the generalised linear model for daily climate time series: GLIMCLIM, and the non-homogeneous hidden Markov model: NHMM) for multi-site daily rainfall were evaluated and compared in the North China Plain (NCP). The comparison focused on a range of statistics important for hydrological studies including rainfall amount, extreme rainfall, intra-annual variability, and spatial coherency. The results showed that no single model performed well over all statistics/timescales, suggesting that the user should chose appropriate methods after assessing their advantages and limitations when applying downscaling methods for particular purposes. Specifically, the CR-SDSM provided relatively robust results for annual/monthly statistics and extreme characteristics, but exhibited weakness for some daily statistics, such as daily rainfall amount, dry-spell length, and annual wet/dry days. GLIMCLIM performed well for annual dry/wet days, dry/wet spell length, and spatial coherency, but slightly overestimated the daily rainfall. Additionally, NHMM performed better for daily rainfall and annual wet/dry days, but slightly underestimated dry/wet spell length and overestimated the daily extremes. The results of this study could be applied when investigating climate change impact on hydrology and water availability for the NCP, which suffers from intense water shortages due to climate change and human activities in recent years.     

Improve the prediction of summer precipitation in the Southeastern China by a hybrid statistical downscaling model

We attempt to apply year-to-year increment prediction to develop an effective statistical downscaling scheme for summer (JJA, June–July–August) rainfall prediction at the station-to-station scale in Southeastern China (SEC). The year-to-year increment in a variable was defined as the difference between the current year and the previous year. This difference is related to the quasi-biennial oscillation in interannual variations in precipitation. Three predictors from observations and six from three general circulation models (GCMs) outputs of the development of a European multi-model ensemble system for seasonal to interannual prediction (DEMETER) project were used to establish this downscaling model. The independent sample test and the cross-validation test show that the downscaling scheme yields better predicted skill for summer precipitation at most stations over SEC than the original DEMETER GCM outputs, with greater temporal correlation coefficients and spatial anomaly correlation coefficients, as well as lower root-mean-square errors.     

Uncertainty analysis of statistical downscaling models using general circulation model over an international wetland

Regression-based statistical downscaling model (SDSM) is an appropriate method which broadly uses to resolve the coarse spatial resolution of general circulation models (GCMs). Nevertheless, the assessment of uncertainty propagation linked with climatic variables is essential to any climate change impact study. This study presents a procedure to characterize uncertainty analysis of two GCM models link with Long Ashton Research Station Weather Generator (LARS-WG) and SDSM in one of the most vulnerable international wetland, namely “Shadegan” in an arid region of Southwest Iran. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. Uncertainties were then evaluated from comparing monthly mean dry and wet spell lengths and their 95 % CI in daily precipitation downscaling using 1987–2005 interval. The uncertainty results indicated that the LARS-WG is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % uncertainty bounds while the SDSM model is the least capable in this respect. The results indicated a sequences uncertainty analysis at three different climate stations and produce significantly different climate change responses at 95 % CI. Finally the range of plausible climate change projections suggested a need for the decision makers to augment their long-term wetland management plans to reduce its vulnerability to climate change impacts.     

Prediction of early summer rainfall over South China by a physical-empirical model

In early summer (May–June, MJ) the strongest rainfall belt of the northern hemisphere occurs over the East Asian (EA) subtropical front. During this period the South China (SC) rainfall reaches its annual peak and represents the maximum rainfall variability over EA. Hence we establish an SC rainfall index, which is the MJ mean precipitation averaged over 72 stations over SC (south of 28°N and east of 110°E) and represents superbly the leading empirical orthogonal function mode of MJ precipitation variability over EA. In order to predict SC rainfall, we established a physical-empirical model. Analysis of 34-year observations (1979–2012) reveals three physically consequential predictors. A plentiful SC rainfall is preceded in the previous winter by (a) a dipole sea surface temperature (SST) tendency in the Indo-Pacific warm pool, (b) a tripolar SST tendency in North Atlantic Ocean, and (c) a warming tendency in northern Asia. These precursors foreshadow enhanced Philippine Sea subtropical High and Okhotsk High in early summer, which are controlling factors for enhanced subtropical frontal rainfall. The physical empirical model built on these predictors achieves a cross-validated forecast correlation skill of 0.75 for 1979–2012. Surprisingly, this skill is substantially higher than four-dynamical models’ ensemble prediction for 1979–2010 period (0.15). The results here suggest that the low prediction skill of current dynamical models is largely due to models’ deficiency and the dynamical prediction has large room to improve.     

New regression model for Indian summer monsoon rainfall

Summary Based on the study of 45 years (1948–1992) data, the average lowest MSL pressure of heat low over central Pakistan and adjoining northwest India of the month of May is found to have potential as a parameter for predicting all India Summer monsoon seasonal rainfall. This new parameter is seen to have stable and significant correlation with monsoon rainfall. Its correlation coefficients for different periods are found significant at 0.1% to 1% level of significance. The stability of the correlation coefficients was tested using 10, 20 and 30 year sliding windows. This test revealed that it is the most dependable parameter in comparison with 7 of the well known parameters analysed in this study. Regression models have been developed considering this new parameter along with other circulation parameters. The regression models developed are seen to perform very well for the independent data. The Root Mean Square Error (RMSE) values of some of these models, for independent data, are smaller than those of similar regression models reported in literature.With 8 Figures     

Future projections of winter rainfall in southeast Australia using a statistical downscaling technique

Much of southeast Australia has experienced rainfall substantially below the long-term average since 1997. This protracted drought is particularly noticeable in those parts of South Australia and Victoria which experience a winter (May through October) rainfall peak. For the most part, the recent meteorological drought has affected the first half of the rainfall season May–June–July (MJJ), while rainfall during the second half August–September–October (ASO) has been much closer to the long term average. The recent multi-year drought is without precedent in the instrumental record, and is qualitatively similar to the abrupt decline in rainfall which was observed in the southwest of Western Australia in the 1960 and 1970s. Using a statistical downscaling technique, the rainfall decline is linked to observed changes in large-scale atmospheric fields (mean sea level pressure and precipitable water). This technique is able to reproduce the statistical properties of rainfall in southeast Australia, including the interannual variability and longer time-scale changes. This has revealed that the rainfall recent decline may be explained by a shift to higher pressures and lower atmospheric precipitable water in the region. To explore the likely future evolution of rainfall in southeast Australia under human induced climate change, the same statistical downscaling technique is applied to five climate models forced with increasing greenhouse gas concentrations. This reveals that average rainfall in the region is likely to decline in the future as greenhouse gas concentrations increase, with the greatest decline occurring during the first half of winter. Projected declines vary amongst models but are generally smaller than the recent early winter rainfall deficits. In contrast, the rainfall decline in late winter–spring is larger in future projections than the recent rainfall deficits have been. We illustrate the consequences of the observed and projected rainfall declines on water supply to the major city of Melbourne, using a simple rainfall run-off relationship. This suggests that the water resources may be dramatically affected by future climate change, with percentage reductions approximately twice as large as corresponding changes in rainfall.     

华北地区夏季降水模拟研究:区域气候模式性能评估

利用高分辨率区域气候模式Reg CM3对华北地区1991—2002年夏季气候进行了数值模拟,对照中国台站的实测资料,对模拟的华北地区夏季降水、温度进行了较为全面的比较,以检验模式的模拟性能。对平均场的模拟结果检验认为,该区域气候模式对华北地区夏季降水的空间分布模拟存在一定的误差,河套地区及黄河以南地区降水量接近实况,沿着太行山脉及东部沿海地区降水量明显偏多。模式对温度的模拟误差较小,较好地再现了气温的空间分布特征,但山西及以北地区模拟的温度略偏低。模式能够较好地模拟出华北地区夏季降水和气温的年际变化,成功再现了该区域降水和气温的异常变化。模式能够成功模拟出该区域降水和气温日变化特征,特别是对于逐年夏季的降水日变化过程的峰值和谷值均有成功表现,对于典型年份华北地区较强降水过程中降水发生的时间、落区、强度等也有再现能力,不足的是模拟的降水量比观测偏大。对于模式误差是否与地形或模式积云对流参数化方案等有关,需要进一步探讨。     

Surface pressure and summer monsoon rainfall over India

The relationship between the all-India summer monsoon rainfall and surface pressure over the Indian region has been examined to obtain a useful predictor for the monsoon rainfall. The data series of all-India monsoon rainfall and the mean pressures of three seasons before and after the monsoon season as well as the winter-to-spring pressure tendency (MAM-DJF) at 100 stations for the period 1951-1980 have been used in the analysis. The all-India monsoon rainfall is negatively correlated with the pressure of the spring (MAM) season preceding the monsoon and winter-to-spring seasonal difference as pressure tendency (MAM-DJF), at almost all the stations in India, and significantly with the pressures over central and northwestern regions. The average mean sea level pressure of six stations (Jodhpur, Ahmedabed, Bombay, Indore, Sagar and Akola) in the Western Central Indian (WCI) region showed highly significant (at 1% level) and consistent CCs of -0.63 for MAM and -0.56 for MAM-DJF for the period 1951–1980. Thus, the pre-monsoon seasonal pressure anomalies over WCI could provide a useful parameter for the long-range forecasting scheme of the Indian monsoon rainfall.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Pre-monsoon surface pressure and summer monsoon rainfall over India

Summary The relationship between the surface air pressure field during the pre-monsoon months and the Indian summer monsoon rainfall is analysed using climate data from 105 stations situated in Eurasia between 0°–60° N and 20°–100° E. Moreover, grid-point data for the whole northern hemisphere are used. Pressure during April over an area around 50° N and 35° E is found to show a significant negative correlation with the subsequent monsoon rainfall. During May the pressure over a large part of the study area south of 40° N shows a significant correlation with its highest value in the heat low region over Pakistan. It is assumed that monitoring of pressure variations over this region may be useful in predicting monsoon rainfall, particularly the rainfall during the first half of the season. Certain limitations of the climate data in this region are also discussed.With 5 Figures